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Kernel principal component analysis of the ear morphology

Reza Zolfaghari, Nicolas Epain, Craig T. Jin, Joan Glaunes, Anthony Tew
2017 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)  
The statistical analysis conducted over the space of ear shapes uses a kernel principal component analysis (KPCA).  ...  In the work presented here we show the morphological variations captured by the first two kernel principal components, and also show the acoustic transfer functions of the ears which are computed using  ...  Examining the Kernel Principal Components We now describe how the kernel principal components can be used to examine important changes in the ear morphology and their corresponding acoustics.  ... 
doi:10.1109/icassp.2017.7952202 dblp:conf/icassp/ZolfaghariEJGT17 fatcat:dnvprr7mdra2hgp7ckxaeq2wfa

Maize Germplasm Characterization Using Principal Component and Cluster Analysis

Solomon Mengistu
2021 American Journal of BioScience  
The principal component analysis indicated that the first principal component (PC1) had an eigenvalue of 4.4 and reflects 48.85% of the total variation, this represents the equivalent of two individual  ...  The characters used for analysis were days to flowering, plant height, ear height, ear per plant, days to maturity, ear length, kernel rows per ear, a thousand grain weight and yield per plot.  ...  4 . 4 The principal component of traits used for cluster analysis.  ... 
doi:10.11648/j.ajbio.20210904.12 fatcat:arqvexqndvd5fcp4d7thcefuay

The Cluster and Principal Component Analyses of Maize Landraces of Manipur, India

Chuwang Hijam et al., Chuwang Hijam et al.,, TJPRC
2019 International Journal of Agricultural Science and Research  
Three broad clusters were formed in the dendogram produced by the morphological data. The principal component analysis of 28 morphological traits accounted for 80.47% of the total variation.  ...  The first principal component explained the highest 26.44% of the total variation.  ...  ACKNOWLEDGEMENTS We would like to thank all the farmers from various districts of Manipur for providing the seeds of maize landraces without which there was no means to conduct the present experiment.  ... 
doi:10.24247/ijasraug201921 fatcat:znarxnxk7bhbdd6mgrb7pryvuu

Estimation of Genetic Diversity in Seven Races of Native Maize from the Highlands of Mexico

Mario Rocandio-Rodríguez, Amalio Santacruz-Varela, Leobigildo Córdova-Téllez, Higinio López-Sánchez, Aurelio Hernández-Bautista, Fernando Castillo-González, Ricardo Lobato-Ortiz, J. Jesús García-Zavala, Pedro Antonio López
2020 Agronomy  
Principal component analysis separated the different accessions into well-defined groups using first three principal components.  ...  Seven maize races of the central high plateau of Mexico were characterized using a combined analysis of 13 morphological traits and 31 microsatellite loci.  ...  Regarding the principal component analysis of the combined dataset (frequencies of 211 SSR alleles and 13 morphological traits), the first 20 principal components explained 55.1% of the variance.  ... 
doi:10.3390/agronomy10020309 fatcat:4pwdo4wkvzbixhyknwhrgwpf2y

Analysis of Morphological Characteristics Among Popcorn Inbred Lines
튀김옥수수 자식계통들에 대한 형태적 특성

Eun-Ha Chang, Kyu Jin Sa, Jong-Hwa Kim, Ju Kyong Lee
2013 Korean Journal of Crop Science  
On the principal component analysis, silk color (QL2), ear length (QN3), kernel setting length (QN4), ear thickness (QN5), ear weight (QN7), kernel weight (QN8) and 100 kernel weight (QN9) greatly contributed  ...  (QN3), ear row number (QN6) and 100 kernel weight (QN9) contributed in negative direction on the second principal component.  ...  On the principal component analysis, silk color (QL2), ear length (QN3), kernel setting length (QN4), ear thickness (QN5), ear weight (QN7), kernel weight (QN8) and 100 kernel weight (QN9) greatly contributed  ... 
doi:10.7740/kjcs.2013.58.3.267 fatcat:oudwnrtoorazvirfagegbaz5oy

Genetic diversity in maize dent landraces assessed by morphological and molecular markers

Danijela Ristic, Vojka Babic, Violeta Andjelkovic, Jelena Vancetovic, Snezana Mladenovic-Drinic, Dragana Ignjatovic-Micic
2013 Genetika  
Cluster analysis of morphological and SSR markers distances did not exhibited the same grouping of accessions.  ...  Applied marker systems revealed high level of genetic heterogenity beetween landraces. Cluster analysis of the tested genotypes was based on average values of eighteen observed phenotypic traits.  ...  ACKNOWLEDGEMENTS This work was supported by the Ministry of Education, Science and Technological Development of Republic of Serbia, through the project TR31028 "Exploitation of maize diversity to improve  ... 
doi:10.2298/gensr1303811r fatcat:2nz4go74tjgrro5vjg7trxt2ua

Classification of French maize populations based on morphological traits

B. Gouesnard, J. Dallard, A. Panouillé, A. Boyat
1997 Agronomie  
The most important variables in the principal component (PC) axis were related to maturity traits, and ear and grain shapes.  ...  On the first plane of the PC analysis, the distribution of populations was continuous, and populations from some particular regions were found grouped together: Pyrenees (early material and conical ears  ...  Principal components analysis Distribution of variables The first four principal components (PC) accounted for 77% of the total variance (table IV).  ... 
doi:10.1051/agro:19970906 fatcat:jdriirwoyvbrfmbgmevr6dp3iu

Variation in Agro-morphological Traits of Some Turkish Local Pop, Flint and Dent Maize (Zea mays L.)

Fatih ÖNER
2019 Notulae Scientia Biologicae  
Eight agronomic and morphologic traits (ear length, ear kernel row number, ear height, leaf number, 1000 kernel weight, tassel length, leaf width and leaf length) were analysed by ANOVA and principal component  ...  Multivariate discriminant function analysis with eight traits revealed that first two of multivariate correlation covered 86.6%, and next 69% of total variation among accessions and the first multivariate  ...  and morphologic traits by principal component analysis.  ... 
doi:10.15835/nsb11110407 fatcat:j7rb2vuajrcqzpxvv4ok5qvdiy

Analysis of genetic diversity among the maize inbred lines (Zea mays L.) under heat stress condition

Manoj Kandel, Surya Kant Ghimire, Bishnu Raj Ojha, Jiban Shrestha
2018 Journal of Maize Research and Development  
Analysis of variance showed significant difference for all the traits. Result of multivariable analysis revealed that twenty inbred lines formed four clusters.  ...  , ear per plant, cob length and diameter, number of kernel/ear, number of kernel row/ear, number of kernel row, silk receptivity, shelling percentage, thousand kernel weight and grain yield in alpha lattice  ...  ACKNOWLEDGEMENTS The authors would to thank to National Maize Research Program, Rampur, Chitwan, Nepal for the provision of reseach support. AUTHOR CONTRIBUTIONS  ... 
doi:10.3126/jmrd.v3i1.18925 fatcat:zgm6vtljsffvrhsu4okhadib2q

Characterization of Selected Maize Inbred Lines Adapted to Highland Agro-Ecologies of Ethiopia Using Morphological and Molecular Genetic Distances

Worknesh Terefe, Adefris Teklewold, Kassahun Tesfaye
2019 Advances in Crop Science and Technology  
The objectives of this study were to characterize elite maize inbred lines adapted to highland agro-ecologies and classify groups of similar inbred lines by means of cluster and principal component analysis  ...  The PCA indicated that the first nine principal components (PCs) with eigen value greater than unity accounted for 83% of the entire diversity among 23 inbred lines for all traits.  ...  the scholarship of MSc for the first author, through the Nutritious Maize for Ethiopia (NuME) project.  ... 
doi:10.4172/2329-8863.1000421 fatcat:saqrkcrkznblxbad5j2gggbvma

Maize landraces as a source of adaptation to climatic change

Violeta Andjelkovic, Danijela Ristic, Vojka Babic, Zoran Dumanovic, Natalija Kravic
2016 Ratarstvo i Povrtarstvo  
Obtained data, together with the results from dry 2012, were plotted for principal component analysis.  ...  Predictions of global warming point out that frequency and severity of temperature and rainfall extremes are expected to increase in the following decades.  ...  ), KW (kernel weight), EL (ear length) and NKR (number of kernels per row) Figure 4 . 4 Principal component analysis of maize landraces based on morphological traits and yield components in different  ... 
doi:10.5937/ratpov53-9138 fatcat:vps2kyhztjddreobwsu6hjov6m

Large scale molecular analysis of traditional European maize populations. Relationships with morphological variation

C Rebourg, B Gouesnard, A Charcosset
2001 Heredity  
The morphological analysis of 19 variables revealed a signi®cant variability.  ...  This study opens new prospects concerning the molecular analysis of very large collections of genetic resources, hitherto limited by the necessity of individual analyses, and proposes a ®rst molecular  ...  We are grateful to Pierre Dubreuil for his critical reading of the manuscript.  ... 
doi:10.1046/j.1365-2540.2001.00869.x pmid:11554974 fatcat:hv2gn3vyvbenpeuep4jqyc3spe

Generating a morphable model of ears

Reza Zolfaghari, Nicolas Epain, Craig T. Jin, Joan Glaunes, Anthony Tew
2016 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)  
In order to extract orthonormal basis vectors from this data, we apply a kernel-based principal component analysis (K-PCA).  ...  Ear # 1 2 3 4 5 0 0.2 0.4 0.6 0.8 1 • Influence of the number of principal components on ear shape reconstruction accuracy: Note: colors indicate local shape mismatch Number of Principal Components 10  ...  In order to extract orthonormal basis vectors from this data, we apply a kernel-based principal component analysis (K-PCA).  ... 
doi:10.1109/icassp.2016.7471981 dblp:conf/icassp/ZolfaghariEJGT16 fatcat:zupvxqurmjaxddpnetg44kivgm

Genetic Diversity Based on Morphological Traits of 19 Maize Genotypes Using Principal Component Analysis and GT Biplot

A. M. M. Al-Naggar, M. M. Shafik, R. Y. M. Musa
2020 Annual Research & Review in Biology  
stressed and non-stressed conditions, using morphological data based on Principle Component Analysis (PCA), (ii) to measure the genetic distance among these genotypes using UPGMA cluster analysis and (  ...  Results of the GT biplot in the present study indicated that high values of 100-Kernel weight, ears/plant, kernels/plant, kernels/row, plant height, nitrogen use efficiency, nitrogen utilization efficiency  ...  Principal component analyses of morphological traits (Table 6) found that the first principal component, which explained 32.39% of the total variability among genotypes, contrasted grain protein, grain  ... 
doi:10.9734/arrb/2020/v35i230191 fatcat:oaj4jq34k5afddbvm6bkacyuta

Genotyping by Sequencing Reveals Genetic Relatedness of Southwestern U.S. Blue Maize Landraces

Amol N. Nankar, Richard C. Pratt
2021 International Journal of Molecular Sciences  
Principal component analysis and tGBS showed that Corn Belt variety 'Ohio Blue' was distinctly different from southwestern landraces.  ...  Blue maize is an important component of the diverse landraces still cultivated in the region but the degree to which they are related is unknown.  ...  Hao Tong of Center of Plant Systems Biology and Biotechnology (CPSBB) for his generous assistance in guiding us through the structure analysis and we would also like to acknowledge the support of CPSBB  ... 
doi:10.3390/ijms22073436 pmid:33810494 pmcid:PMC8037273 fatcat:kzdsprlhgff2hlu47e7r4si5w4
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